{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy_df=pd.read_excel('./体测分数_男生.xls')\n",
    "boy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 477 entries, 0 to 476\n",
      "Data columns (total 17 columns):\n",
      "班级           477 non-null int64\n",
      "性别           477 non-null object\n",
      "男1000米跑      477 non-null float64\n",
      "男1000米跑分数    477 non-null int64\n",
      "男50米跑        477 non-null float64\n",
      "男50米跑分数      477 non-null int64\n",
      "男跳远          477 non-null int64\n",
      "男跳远分数        477 non-null int64\n",
      "男体前屈         477 non-null int64\n",
      "男体前屈分数       477 non-null int64\n",
      "男引体          477 non-null int64\n",
      "男引体分数        477 non-null int64\n",
      "男肺活量         477 non-null int64\n",
      "男肺活量分数       477 non-null int64\n",
      "身高           477 non-null int64\n",
      "体重           477 non-null float64\n",
      "BMI          477 non-null float64\n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 63.5+ KB\n"
     ]
    }
   ],
   "source": [
    "boy_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       良好(60-80)\n",
       "1       良好(60-80)\n",
       "2       良好(60-80)\n",
       "3       良好(60-80)\n",
       "4       良好(60-80)\n",
       "          ...    \n",
       "472     良好(60-80)\n",
       "473     良好(60-80)\n",
       "474    优秀(90-100)\n",
       "475     良好(60-80)\n",
       "476     不及格(0-59)\n",
       "Name: 男跳远分数, Length: 477, dtype: category\n",
       "Categories (3, object): [不及格(0-59) < 良好(60-80) < 优秀(90-100)]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分箱操作\n",
    "bins=[0,60,80,100]\n",
    "labels=['不及格(0-59)','良好(60-80)','优秀(90-100)']\n",
    "db=pd.cut(boy_df['男1000米跑分数'],bins,include_lowest=True,\n",
    "right=False,labels=labels)\n",
    "dt=pd.cut(boy_df['男跳远分数'],bins,include_lowest=True,\n",
    "right=False,labels=labels)\n",
    "boy_df['男1000米跑分数等级']=db\n",
    "boy_df['男跳远分数等级']=dt\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男跳远分数等级\n",
       "不及格(0-59)      43\n",
       "良好(60-80)     351\n",
       "优秀(90-100)     69\n",
       "Name: 班级, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=boy_df.groupby(by='男1000米跑分数等级')['班级'].count()\n",
    "data1=boy_df.groupby(by='男跳远分数等级')['班级'].count()\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男子1000米成绩饼图')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.pie(x=data,labels=data.index,autopct='%0.1f%%',shadow=True)\n",
    "plt.title(\"男子1000米成绩饼图\",\n",
    "         fontsize = 32,\n",
    "          backgroundcolor = '#c5b782',\n",
    "          color= 'white',\n",
    "          pad=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男子跳远成绩饼图')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.pie(x=data1,labels=data1.index,autopct='%0.1f%%',shadow=True)\n",
    "plt.title(\"男子跳远成绩饼图\",\n",
    "         fontsize = 32,\n",
    "          backgroundcolor = '#c5b782',\n",
    "          color= 'white',\n",
    "          pad=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 593 entries, 0 to 592\n",
      "Data columns (total 17 columns):\n",
      "班级          593 non-null int64\n",
      "性别          593 non-null object\n",
      "女800米跑      593 non-null float64\n",
      "女800米跑分数    593 non-null int64\n",
      "女50米跑       593 non-null float64\n",
      "女50米跑分数     593 non-null int64\n",
      "女跳远         593 non-null int64\n",
      "女跳远分数       593 non-null int64\n",
      "女体前屈        593 non-null int64\n",
      "女体前屈分数      593 non-null int64\n",
      "女仰卧         593 non-null int64\n",
      "女仰卧分数       593 non-null int64\n",
      "女肺活量        593 non-null int64\n",
      "女肺活量分数      593 non-null int64\n",
      "身高          593 non-null float64\n",
      "体重          593 non-null float64\n",
      "BMI         593 non-null float64\n",
      "dtypes: float64(5), int64(11), object(1)\n",
      "memory usage: 78.9+ KB\n"
     ]
    }
   ],
   "source": [
    "girl_df=pd.read_excel('./体测分数_女生.xls')\n",
    "girl_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[0,59,79,89,100]\n",
    "labels=['不及格(0-59)','及格(60-80)','良好(80-90)','优秀(90-100)']\n",
    "db=pd.cut(girls_df['女800米跑分数'],bins,include_lowest=True,\n",
    "right=False,labels=labels)\n",
    "girl_df['女800米跑分数等级']=db\n",
    "t=girl_df[\"女800米跑分数\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "女800米跑分数等级\n",
       "不及格(0-59)      62\n",
       "及格(60-80)     280\n",
       "良好(80-90)     168\n",
       "优秀(90-100)     65\n",
       "Name: 班级, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=girl_df.groupby(by='女800米跑分数等级')['班级'].count()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女子800米成绩分布直方图')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r/x8DPa0LPka0GyPRLIWFsMiysSvZ7ZeRmcf2vZ8iN7+CUCjIiYPfpbRqC5VrdrHt5k9y/K3v0N9zlqZdP09x+XomJ0Y5euDRtEzVvBopwJfZ6UOPJxzoVF6zg027HwLgzJEn6brw7rzH2nHLr1FYUo9zIQ6+9NVlWShhulBo+X4R+y6fYmykh+zcUtZte4CMzFwudxxlcnyQzJwiKmp2xGpUI4OXuHxhZjN7dm4JEP5CHxm6lPB9hiNfntHXL9f+mdmFZGblh6fehYIpjcLNzCqMPc7IyE1hOpEPM4vUbAOMj/bHXVAjlZG4s01NjhIKBfH5/LGLxOnqN+6jvukezh57Km4XR/fFwzRuvh8zY3igk5HBTqamxqlpuBkzHy1Hf8Tk+BAbd344pRamRH3g08Wb65udW8q25k8QiFyEXDj7KsMDHfh8gXCgLfDcjI/1c+i1/83mPb9IUWTthaq6Gyir2kLriWfmTA2craB4baxlYmToMudOPjvnNdFAW+5gW87pTstRNl+k6y+0gtOwJidGuHzhEGub3sOpd39Az6Xj9F4+RWZWPsXl69l20ycYHrpEfmE1U1PjHD3wLUaHL69Y+VabAnwFRRcGmZwY5vLF+QfAmPnIKwxP5Rrqv5A0vM38BDKyCQYncLEAmt9Cf6mjU0ACgSwCGTlMTgzFajHOhTj8xt+zafdHKSiuo2Hze2nY/N45x+i9fJpT73xvThN3RmYuMHN50niigZeZVRCpoYWWZf+quhvmHZSVTOOW+2nccv+C9kk0Enz6hUOyMRKjIz2xUBsf7Scnr3TOhURB8drYNKwN23+Owb62OdOXJidGOPz6NxgZuhSrFTdGput0XTgUC33nQkxNjkVq4MEFDweKzQUPZM75/OXklbPt5k/Gynq54wih4CR73/tvF/guc73+zF+xduPd1DSE+7HN52dkMPGFHoQ/I5v3/GK46dyFaD3xTGwMwHTRqYJmvqQXcVOTY6szIn4ZRl5HA3xl51E7zp95ke7OY7FgdqEgx9/6Djfc9a/Jyi4kvzB8Q5Uzh59Y8Nr9XqcAXyGZWQUUla0DoPP8waRTb/KLamOD3Xq7TiU9fkFJHTv2fnrR5VvMvrMDaHy0n8sXD5NfVBt3run4aD+Xzr8dt9YZ/RKcTNJUGu07N5+PjKz82LSbpe5/NZleA99z12/P+9rXnv5/Y82FYyM95OSVkj1rLemquhtij1uPPzMnvKMG+670gZZWbaGmYS9jI72cOfLD2Pb5WpiWas36O2KtBwO95zj1zvdirTZLMTU5xtTkCC1Hf8RgXxvrtz9I28mfMdh3PuE+/kA2W278WCywzXxsvfFj875PQXFd0hXRUhlMdsWVz8FSfrfDR1pagPt8gVj3lz+QuajBt5MTI4tuRYyGt5mf8prt1G24M/ZZifaLN+38eUorN9PZ9mbCdSauNQrwFVKxZldk8YFgSiualVVvjT1ezekq00XXaAYLD1qb9Z2wYccHY6tUxZOVU8TmGz7KQE8zR998NG5NZPpa3vG4aTX3eCNZF7t/5/mDdHccY3JyJLzISWgqbq0lK6eIPXd9Fp/PT3fnccqqNgPhL6eMzFyGBjp455X/GX8Qn8+Pz/z4M7LDLRgJRj2nekvMUCg4o69vbCQ8Hzgvv+rKvzGQRVn1NiDcddGRpMkYoKBkLU27fh7nQpx4+5/i/j+VVG5iXaRpeSHGR3o5/Mbfx33u1LvfY3S4m7KqLeGb+4Sm6O8+Gx5TEpzEhcLdGo1b308gkMXwQCcXWl9N+F5rN9xFdm7pjNHlly8eZrCvfd4BiYGMHLbd/Kuxmt1ycotcxnNifGhRS4Au17zuQKSFC6C6vpnq+uYFH+P04SeSdlnEY+aPDRQsr9kem+46NtJLy9EfMzbSS+PW91FSvoHymu2U12xnYmyQvu4zDPS0MjLUlbS1xasU4CvAfH5qIlNQujtTu31oaWQ1rMG+9pRGfA72nueNn36BYHASF2eJy3gKSxtiV/aLHSQTVVa9bUZ493efpfvSMcaGe8jKKaKidmfsBhCFpfU0brl/Rk0u2q+WbCDQ9H776Kjz5dh/YmyACZL/v9Q23obP5ycUCtJ+5sVYgF9oeYU16+8gv7CauvV3xb29pAsFCRIkGJyY9zMQHdHvXIhXfjz3rkn1TfdQt+HOOVPBRoa6AMjOK8XMj3NBqutvjg0YO3fqZyTr1y+t2kLTzo/g92fQcvTHDPVfICMrn5zcUrJzS+m7fDociG5x4RBv+tp07Wde5ELLy7HP70DvOQZ6r4zkN/OzYUd49sZQf3uCLoiwuvV3AeEBoNPNF94ZmXlsu/lXY0sd9/ecJSe3jMzsAtpO/Yy2U3P/X6PjAuabFXJlMNniAvzE2/+0pEFsS5WZVbDkYyRq+ZnP+u0PUrlm94wVBifGBmlveZmxkV4mJ4YZHb7M0f3foqBkLXXr76C4fCOZ2QVUrtlN5ZrdjI8N8PZLX11y+a9GCvAVULXmhli/3sWWxDWGqOLyDbEvx1SXWnUuuOQ7DC1FtG8R4MyRH9FxbuYUo862N6luuDk2X7Widietx38aG10cHRFvvvlrn75pz/t8GbHHS90/Fdl5ZbG1zbs7j8443xMTQ7Sd+hnrtr6ftRvvYrDvPP3dZxZ0/Ch/ksFC0UCe3V0QXarV5/OTV1TNyOAlahvDc+OH+i8kXUJ28w2/GGv5cS5EVd0e6pvuib0fhOeYT4wPzgiieEuCzhYNuVQGQM138ZmVW4xZeDR0svEO0ebvVJdSzS+sCa+LEOnHHuw7z7ED/8DuO38rpf1TsZIDwJZTXuGVVp0j+79N3+Xk3XoQrq2v3/YBYO6FVCraT79IZe0unAsx0HuOzra36O44gpmPPXf9Nlk5RQz1X6Cj7QCXLx7m6IFHycoppqpuD2XV28jJK6Xl6I8T3hLY6xTg6WZGbaQfLxQKUlyxkfGxgXnDNlpbn5wYXdZbFaaLmY+CovBCLj2dx+eEd1RH6xvkF9ZSuWYXPl+A3IJKBiJ9VdHzkaxWF5g2RWr6F/1S90/F+q0PhKfvOUf76blLo15sfZ2yqq0Ultaz+YZf4PDr32B4sCPOkeYXvddyosFC0ednT4MaHugkFJzC5w9QWFwfuRlLuLmx9UQKC29EgjH80EdupBbqXIiJ8SEmx4djFw2JxnDkFlSRk1tKMDjBUP/FOdO/ljoaOi+/MvZ4vtkGPn/mlSmYKawoWFl3A+u3fiDWKjPQe46j+7+ddPW2hVrszUuW2ge+VHkFVwJ8vvM+W8a0QX+LqWCMj/Vz5MC3GR3qmrF/bkEVU1NjZFFEflEtG4tqadzyProvHqHt1M84d/JZzp18ltz8iljL1LVIAZ5m2bmlsQUZfD4/9U37qNtwFz2dx+g4t39G8yCE58AWR5Yk7Ww7sOxzS9MhkJkbq/n2Jal19nadjI3Gz8zKi22P1uCykgTw9Ka86fPYl7p/MmvW3xFbg/nyxUOMDF2KO+L45LvfY/ftvxnpR/0ERw88uuCRsdER9YkG/ET77qcmZj7vXJDB/naKShsor9lGbiTsertOpTQnvffSSXJyyxjsa2N48BKjw5cZG+llYmxgTvBG5wTPVlm7k9p1txEKBTnw3BeTvudCFZY2xB4P9SdeiGf6aPFUuqxc7N7bAXovn+b4W9+5qn73VrsPvLAkfN7HxwYWNPAzM3IB6Vxo0XddHIgzIG144CJvv/QIhSX11DbeSknlJgKBLIorNtBy9Eex113L4Q0K8LQbG+7m4EtfIb9oDZV1N1BRswN/IDM22GJksIuOtv10tb9DMDhBw+b3YmZMTY5x4aw37hOebODYDNMGd01v1op+GWdk5pKVXZRw8ZSCkrrY47FpzbZL3X8+ZVVbqd+4L1bms8efTvja8dE+Trz9z2y96Vcii1B8mpYjTy6oJSV6kZGo2S9as4y3uEnf5dMUlTaQX1QLhJe2TfU+zZfaD8ZdgCeuRHe0inQVDQ9cjFvjSnWAXiIlFRsAGBnsmndxl5kBnrzptuvCu4wOXaay7gZajv541e9RMNtq9oHnF9aQWxBe+nihZciIdWMMsVxr/K/d+J5IF9XZ2BiJ7Lwy1qy7nZHBzmvqft/JKMBXyFB/O0P97bQee4qKNbuorm8mN7+C3IIK1m/7AA2b3kvf5dOxG4KcP/3ColafWg3BqTGmJkcJZOTMWSBltmiwwMyr4+k1xNKqLVxsjX/xUhhZjGNstG9GDXqp+ydSXrODjTs/HGthOH3o8aQ3eem7fJrTh55gw44P4vdnsHHnhymr2srZ40+ntMhETuSGLIkuQqLL0U5OzK2h93Qep2HTvbGfz518LuUlYBciOm1vtryC8MjtRFO04t3uNFXF5Rtid5rr7jw672unN92m2gc+NHCRoSPpu+WrV1VOm4bYc2lh64vHxiEsov87noLiOtZuDN8bPjg1Tm/XKbo7j9HbdZLT025edL1QgK+wYHCCjnP76Ti3n+LyjaxZdxtFZY34A5mxAUShUJCBJOsSX236ulsor95GScUmWo8/E7cGE8jIpbx2BxDuY5zeFDc+1s9g33kKiuuoadwb7j6YdSWdX7QmNrVndpPwUvefzSLdHWvWXZmHfP70i0mDI+pSe/j+6Rt2PIjPF6Cksoni8g10dx7lYuvrCQMuM6sg1oQ+PhL/NpHR+a/xLvCiN5WB8IjneDVqnz8jafOwmY/M7CJy8krJyS8nN7+CvIIqertO0XbqZ7FFPabLzi2N3Q0u0efXt8CbWEwX/eJ2ziVt0VhoDXw+iboLFiI6V3mxVqsPPCunmMo1NwDhMRc9naneUCYsOgZjudZbCGTkMNDbRkHxGvyBrFhLZnBqgp5LJ+i68DZ9lxc3eNSLFOCrqO/yKfoun6Kqbg8bdnwwtt3n87Prts/Qe+kkbWdeYKivfZ6jXB3az7xEaeUmcvJKadr1EK3Hn55Rgywsqadh831kZRcSCgU5e+ypOce42LqfguI6snOK2bDjg5x853tEm90CGdlsnHaOLp2f+wW+1P2jSqu20LDp3hmLVbS3vBJ3Gc35dF14h9GRbjbteojs3BLM54t94YyN9HLu5HNzljWNrrkNMBxn7mpeYXVsRsPsPsXi8vWs3/5zsZ99vgBr1t/BuVkD2Pbc+a9oPfHTOff7rqjdQUZmPplZ+WRk5cUNnI7I/biH+i9w8t3vzShHdCZCKDQV6bc0Sis30XPpBO1nXlrSevvVDTfHLk66O44mbVWI1sCdCzExkfptceOxJbQaRMsSO5ceuxd145b7YwP7LrS8suBBeBmRcS7jKQa4Mf9FTm/XSXq7TpKRmUtp1RYqandRWLIWfyCTitrwZzjZlNilduNcTRTgq6y0agv1m8JLjjoXorvjGCWV4VuOllQ2UVLZFKn1PD/jzjtXm+GBDk68/c807fww5TXbKKvewsT4EMGpCbKyC2Ijp0OhYMIlDy9ffJeahmYKiuuoqN1JTl45XRffxe/PpGrtjbGaZ0/n8Rmrhi3H/j5/BrWNt0b2uxLcodAUZ448yaXzBxd1Xob62jn40iM0bn4vVWtvjH15ZGTmxb37U3H5xtjjaBk3bP8gWTlFBDKyYwPTIDy+Iqq0cjObdn8Un8/P5MQoo8Nd4QE+Dbdw+cKh2MjhrOwisnKK2bT7oxSXb4itRR4KTsbuUz/d5MQoo0NdsQFt0VvNjo/20tV+pYUgr7CaqrobAejuOMbU5Bjrtz9I9dobGR3upuXoT1KeejRbUdk6GjffB4T79M8e+0nSfaLjCCbHh5ccmotp9q+o3UVOfjkZmbkUTRt4NzG+uKmeS72ZyWKU12ynLLIexdhoHxcSdEslEsjIjnW1pNwKYqm1dkxOjNDZ9iadbW+SnVtK1do9VK7ZzUBvW9LzpACXJSsoXkvdxrsoKQ8PyglOTXDq0ON0dxwmIzOP2nW3Ur22GX8gk5KKjZRUbKS36yTnTjy3qKlJ8QTi3LVqKXo6j/Fm33mq1zZTXL6O7NxSMnLzCIWmGBm6zEBPKxdbX5+3H/jYm//A1ps+Tn5RLflFNeQX1cx4fnigk5OzboCxHPuHgpNk55bOCO/BvnZOH/rBkkeyhoLhgWQdbW/SuPm9FJdv4ELra3P6uAMZ2bEvzNHh7tjKaiNDl2Lzz6/8Ozpit0Nds+526jfdg5mPyYkRjuz/JsHgJDfc8Vv4/AG23PjLvPPK15iaHI1NDYPwbUOj+ntacS7E2EgffZdPM9DbxlD/BcZH4zfjT1exZhfrtrwfnz98v++2U8/F7pbnXIicvDK2NX+c7s5jnDnyZNIxBNMVla1j855figXB6cNPpNSnHR1rkWr/93zMkgW4zfo73D9bt/6OGa+anBiOe+F5NcotqIotmONciDOHn1jwGui506aeTYynWANPMcCnGxvpofX4M5w78Vzcrp2576EAl0XIyMqnvHobFbU7Zwzm6u9p5czhJ2Irrk1ODNN6/BkutLzK2o13U1m3B5/PT0lFE8XlG+nuOLKg+1NH+QPZ+Hx+zMLrgNc23BJ7LjS1PPNdJ8eHaDv1HG2nnlvc/hMjvPvq/6a28RZqGvbGmounJsfoPP8mbaeen7cZdin7nzn8BAXFdfgDmZw//UKkuXi+2tu0edMp9JOODHZyZP+3yC9eM2cKGEBNw97YCPPpK4z1XDrO2qZ9TIz2MzJ8mYHec3S1v0NGVj5NOz8Sm942NtLL0QOPxi6QLrS8Qt2GO8nOLWH73k9x7M3HYq8FZrToBKfGOPCzv065rzIzq4Cy6q1Urb0p1u89NTXO8Tcfi114tBz9EV3tb7Nhx4fIK6yirGoLRaUNtBz9SfIFisyobbyVhqZ7YwMI43U5zObzBahcuydWpuW4b3WiAXtXiuqb8TeEm3qDU+P4A1kEg5P0d7dw7uRzCT970dXupt8Nb3o/flZ20aLWH4/KzCogJ68sfEMi8+PzZzAxPhj3OyQrp5itN34stqDQuZPPLbhf2efPoL7pntjPYyNz3ye+8O9RdBW1dFCAS1JmPrLzysgvrKGguI7C0obYl0rU8EAHbadfSLhC1uTEMGeOPMmFs6+ybsv7Kalswswor9lOSeUmDr74lQWFeFFZI1v2/NKc7aFQMHxXq6uEc0HaW16mveXlcN+x+Rgb6U15as9i9w+Fpjj+1mOREerJ+2qnfxEs5Esh3piGnLxyaiMD5oJTE3Sefyv23PhoP68//fkZ71Vd30zdhrtiA94Gets4/tZ3ZkzdOnfqOQpLGygsWUteQRU33vXZ2DVHeKDkzDUIkoV3Re0u8otqKSxtiC01GtXf08rpQ48zNutzNDRwkXde+Z+s3fge1qy/g0BGDk27PkJp1WZOH3oi7kC84vINNGx+74zFQ84ef5oLLa/ELVdWThG7bvsNfL5ArKsmqjvJ6nOpOPzGN8As4e9a9AJjelO7cyGOvvkPBCfHGRnqStp3XFy+nqadH0n4fNOuxM+lomHTvTNmJwBxl4bNza9ka/PHY91Nneffov3M3EWLpsvKLooMTg3f5z07p5iisnWxz+bkxAjDA6m1Gia7WFoOCnCZV9Xam1i39f1x+85CoSl6Lp0I3zEnhcU1IFKzevNRSqu2sH7rA2RmF9DR+saCa+A9nccYG+mdMdXLuRBtp362Orc4TMFYgpHY6dp/Ic3l05vrUmm6m8+6re+P1Xgunnsj4apVPn8GO2/9TCxAnXNcOPtKeKW12X29znH0wKNsa/4EBcVrZiwz29/dsuD/84ysvBlL5joXou/yGS62vh7rG4/HuRDnTj5Lf08rm3Y/REZmLrn5FeHBUbOuk7bc+DFKKzfFfp6aHOP04SfmvaFP+C54R2aUDeDC2deSLh+bitkXOrPFbic6K3wW1GftIBicjNy0JVwDX+odxOIJ18B9+P2Zc+47X1m3h3Vb3hdrBeo8fzClO8+Njw1QuWZ33BaCUCjImSM/SnnwW3TAYHfHUc4enzvQdSlqGm6htvGWpMste4kCPA062w5QUFwXW3FsfLSP/p5z9F0+Te+lE4tenrGn8xgDPa00bL6Ptjg3y0jFxdY3yC+qYWy0j5HBSwz0tK7qGupe5l/GAD/21mPUNtxCec0Ozse5YUZUKDjJqXe/x469v8bkxDCnDz0+760Tg1NjHH7jG9StvzPWRB8KTnH+9AsLLuOFllcoKF5LIJBJz6UTdHccXVAfc3/3Gd555Ws0bLqXlqM/irsy16l3v8+OvZ8mt6CSvu4WTh96PKUL1e7OY5RXb2N0pCd8I52Owyu2Clc0wJcyx73rwjsp3/cgbVwo1kp14eyrcWeKJNiR3q6TsQAPBicZGeykv/ssl9rfntMqM5/ovQqCwYlF3fxkPtE7911LNXCbfRUmqXn5R38674nLyMwlt6CKkcFLCkhJnVlKo6YLiusYHuxc0LQsny9Adl4ZE2P9V/XNHbLzysjLr0x5zr0sn9z8CgpK1i74tp/ZuaVk5RQzPtq75Fazq8XtD/yHpS8AkGaqgafJ5MRIyk3kIjEpXlAnWghmPqHQFCODnQveb6WNDXfPmCInK2dkqGtRLRdjIz0LqmnL8rh22hJERESuIwpwERERD1KAi4iIeJACfPGu/s5EERFZDE98v2sUuoiIiAepBi4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDwokM6Dm1kV8CPn3B4z+xqwFfihc+7PIs/P2Taf8vJy19jYmM4ii4iIXDUOHDhw2TlXEe+5tAY48JdAjpl9FPA75243sy+ZWROwc/Y259zJ+Q7W2NjI/v3701xkERGRq4OZtSZ6Lm1N6GZ2LzAMdAD7gMciT/0UuDPBtnjHedjM9pvZ/q6urnQVV0RExFPSEuBmlgn8R+BzkU15QHvk8QBQlWDbHM65R5xzzc655oqKuK0IIiIi15101cA/B/ytc64v8vMQkBN5nB9533jbREREJAXpCs37gM+a2XPADcCHuNJEvhs4CxyIs01ERERSkJZBbM65u6OPIyH+YeAFM6sFPgDcCrg420RERCQFaW+2ds7tc84NEB609ipwj3OuP962dJdFRETkWpHuaWQxzrlerow6T7hNREREktPAMREREQ9SgIuIiHiQAlxERMSDFOAiIiIetGKD2EREZPk0fu6J1S6CxHH2Lx5csfdSDVxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxoLQFuJmVmtn9ZlaervcQERG5XqUlwM2sBngC2As8a2YVZnbOzJ6L/NkZed0fm9kbZvY36SiHiIjItSqQpuNuB37POfeqmZUAnwG+7Zz7/egLzKwZuJNwyP++md3nnHs6TeURERG5pqSlBu6cezoS3ncTDuhR4CEze9HMvmlmAeBu4LvOOQc8DdwV71hm9rCZ7Tez/V1dXekoroiIiOeksw/cgI8Bk8DbwHucc3cCfcDPAXlAe+TlA0BVvOM45x5xzjU755orKirSVVwRERFPSVuAu7DPAi8D1c65i5GnjgFNwBCQE9mWn86yiIiIXGvSNYjt983s05Efi4GvmNluM/MDDxGukR8g3AcOsBs4m46yiIiIXIvSNYjtEeAxM/tN4BDh/u5vAgZ83zn3tJn5gP9iZl8EHoj8ERERkRSkJcCdc73A/bM275r1mpCZ3Qc8CHzROdeSjrKIiIhci9JVA0+Jc24U+MfVLIOIiIgXaeCYiIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIelLYAN7NSM7vfzMrT9R4iIiLXq7QEuJnVAE8Ae4FnzazCzL5mZi+b2R9Ne92cbSIiIpJcumrg24Hfc879OfBj4F7A75y7Hag1syYz++jsbWkqi4iIyDUnkI6DOueeBjCzuwnXwkuBxyJP/xS4E9gTZ9vJ2ccys4eBhwHq6+vTUVwRERHPSWcfuAEfAyYBA9ojTw0AVUBenG1zOOcecc41O+eaKyoq0lVcERERT0lbgLuwzwIvA7cCOZGn8iPvOxRnm4iIiKQgXYPYft/MPh35sRj4C8JN5AC7gbPAgTjbREREJAVp6QMHHgEeM7PfBA4B/wI8b2a1wAcI18gd8MKsbSIiIpKCdA1i6wXun77NzPZFtn3eOdefaJuIiIgkl64a+ByRUH8s2TYRERFJTgPHREREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPCgtAW5mRWb2pJk9ZWb/bGaZZnbOzJ6L/NkZed0fm9kbZvY36SiHiIjItSpdNfBPAl9wzt0PdACfA77tnNsX+fOumTUDdwJ7gfNmdl+ayiIiInLNSUuAO+e+5Jx7KvJjBTAFPGRmL5rZN80sANwNfNc554CngbviHcvMHjaz/Wa2v6urKx3FFRER8Zy09oGb2W1ACfAU8B7n3J1AH/BzQB7QHnnpAFAV7xjOuUecc83OueaKiop0FldERMQzAuk6sJmVAn8N/ALQ4Zwbjzx1DGgChoCcyLZ8NKBOREQkZekaxJYJPAb8gXOuFfiGme02Mz/wEPA2cIBwHzjAbuBsOsoiIiJyLUpXrfc3gJuAPzSz54DDwDeAg8ArzrmngReBPWb2RSKD3NJUFhERkWtOWprQnXNfBr48a/Mfz3pNKDLy/EHgi865lnSURURE5FqUtj7wVDjnRoF/XM0yiIiIeJEGjomIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDkga4hTUneG7d8hdJREREkkm1Bv49M/uCmf0bM7sZwMxuAr6VvqKJiIhIIoH5njQzc845MzsFfBVoAD5gZl8BJoBfWoEyioiIyCzzBjjwpJkNAUXARuAmoBn4AbAdqADOp7WEIiIiMkeyAP9FoBH4LPAnwEXgI865oJk1An9nZu91zrm0llJERERmSBbgv0245t0NvAP8N6DFzL4J7AB+X+EtIiKy8pINYiuM/H07kEU48N8GDhKumR9OV8FEREQksWQB/hTQAuwBjgEPAbuBB4DPA/8+3k5mVmRmT5rZU2b2z2aWaWZfM7OXzeyPpr1uzjYRERFJLlmA30t4tPnfAJuBbwCngFedc98ANphZvGN8EviCc+5+oAP4FcDvnLsdqDWzJjP76Oxty/NPEhERufbN2wfunPtPZpZHeBR6MPL6P3LOvWxmFcB/d86F4uz3pWk/VgC/Srj/HOCnwJ2Ea/WPzdp2cvaxzOxh4GGA+vr6lP9hIiIi17KkC7k454adcxecc53OuXbn3MuRp/6ScFN6QmZ2G1ACtAHtkc0DQBWQF2dbvPd/xDnX7JxrrqioSPoPEhERuR4sai10M/tdwqPT/3ye15QCfw18BhgCciJP5UfeN942ERERScGCQtPMys3sa8A9wAPOuYkEr8sk3Dz+B865VuAA4SZyCA+CO5tgm4iIiKQgYR94ZHDarxOuKRcAtwBrgK86576f5Li/QXjVtj80sz8E/hfwKTOrBT4A3Ao44IVZ20RERCQF8w1iywQ2AFOE+6vXEh7Mtia6RnqiHZ1zXwa+PH2bmX0fuB/4vHOuP7Jt3+xtIiIiklzCAHfOjQF/OH2bmRUCv0u45vwLzrnOVN/IOdfLlVHnCbeJiIhIcgvqA3fODTjn/gz4d4RvdFKcllKJiIjIvBY18jsylezzwJeSvVZERESWX7KbmSTknHvUzN5azsKIiIhIapY099o5d3y5CiIiIiKp0+IpIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8aDAahdARK5+jZ97YrWLICKzqAYuIiLiQWkLcDOrMrMXIo/XmNl5M3su8qcisv1rZvaymf1RusohIiJyLUpLgJtZCfB1IC+y6Rbgz51z+yJ/uszso4DfOXc7UGtmTekoi4iIyLUoXTXwIPAxYCDy863Ab5vZK2b2XyPb9gGPRR7/FLgz3oHM7GEz229m+7u6utJUXBEREW9JS4A75wacc/3TNj0J3O6cuw3YZGa7CNfO2yPPDwBVCY71iHOu2TnXXFFRkY7iioiIeM5KjUJ/2Tk3Hnl8DGgChoCcyLZ8NKBOREQkZSsVmj82sxozywXeDxwCDnCl2Xw3cHaFyiIiIuJ5K1UD/2PgWWAC+Ipz7riZXQReMLNa4AOE+8lFREQkBWkNcOfcvsjfzwJbZj03YGb7gPuBz8/qMxcREZF5rOpKbM65Xq6MRBcREZEUaeCYiIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPEgBLiIi4kEKcBEREQ9SgIuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIB6UtwM2sysxeiDzOMLPHzexlM/tMom0iIiKSmrQEuJmVAF8H8iKbfgfY75y7HfigmRUk2CYiIiIpSFcNPAh8DBiI/LwPeCzy+GWgOcG2OczsYTPbb2b7u7q60lRcERERb0lLgDvnBpxz/dM25QHtkccDQFWCbfGO9Yhzrtk511xRUZGO4oqIiHjOSg1iGwJyIo/zI+8bb5uIiIikYKVC8wBwZ+TxbuBsgm0iIiKSgsAKvc/XgR+a2V3ANuA1ws3ns7eJiIhICtJaA3fO7Yv83QrcD7wE3OecC8bbls6yiIiIXEtWqgaOc+4CV0adJ9wmIiIiyWngmIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcpwEVERDxIAS4iIuJBCnAREREPUoCLiIh4kAJcRETEgxTgIiIiHqQAFxER8SAFuIiIiAcFVuqNzCwAnIn8Afgd4BeBnwNec879m5Uqi4iIiNetZA18F/Bt59w+59w+IAu4E9gLnDez+1awLCIiIp62kgF+K/CQmb1oZt8E7gW+65xzwNPAXfF2MrOHzWy/me3v6upaweKKiIhcvVYywN8A3uOcuxPoA3KA9shzA0BVvJ2cc48455qdc80VFRUrUlAREZGr3Yr1gQPvOOfGI4+PAZmEQxwgHw2oExERSdlKhuY3zGy3mfmBh4A8wn3gALuBsytYFhEREU9byRr4nwDfAgz4PvBnwAtm9kXggcgfERERScGKBbhz7hDhkegxkZHnDwJfdM61rFRZREREvG4la+BzOOdGgX9czTKIiIh4kQaOiYiIeJACXERExINWtQl9tTV+7onVLoLMcvYvHlztIoiIeIJq4CIiIh6kABcREfEgBbiIiIgHKcBFREQ8SAEuIiLiQQpwERERD1KAi4iIeJACXERExIMU4CIiIh6kABcREfEgBbiIiIgHKcBFREQ8SAEuIiLiQQpwERERD7qubycqVx/d4lVEJDWqgYuIiHiQAlxERMSDFOAiIiIepAAXERHxIAW4iIiIBynARUREPGjVA9zMvmZmL5vZH612WURERLxiVQPczD4K+J1ztwO1Zta0muURERHxitWuge8DHos8/ilw5+oVRURExDtWeyW2PKA98ngA2Dj7BWb2MPBw5MchMzu+jO9fDlxexuNdj3QOl07ncOl0DpdO53AZ2P+z7OexIdETqx3gQ0BO5HE+cVoEnHOPAI+k483NbL9zrjkdx75e6Bwunc7h0ukcLp3O4fJYyfO42k3oB7jSbL4bOLt6RREREfGO1a6B/wvwgpnVAh8Abl3d4oiIiHjDqtbAnXMDhAeyvQrc45zrX+EipKVp/jqjc7h0OodLp3O4dDqHy2PFzqM551bqvURERGSZrHYfuIiIiCyCAlxERMSDrtsA1xKuC2dmRWb2pJk9ZWb/bGaZOo+LY2ZVZvZW5LHO4SKZ2ZfM7EORxzqPC2BmJWb2QzN7wcy+Etmmc5iiyO/wC5HHGWb2eOTcfSbRtuV2XQa4lnBdtE8CX3DO3Q90AL+CzuNi/SWQo8/i4pnZXUC1c+4HOo+L8ing751zdwEFZvbv0DlMiZmVAF8nvBgZwO8A+yPn7oNmVpBg27K6LgMcLeG6KM65Lznnnor8WAH8KjqPC2Zm9wLDhC+C9qFzuGBmlgH8D+CsmX0EncfF6AY2m1kxsBZoROcwVUHgY4RXEIWZn7+XgeYE25bV9Rrgs5dwrVrFsniOmd0GlABt6DwuiJllAv8R+Fxkkz6Li/Np4AjweWAv8Fl0HhfqRaAJ+F3gGJCFzmFKnHMDs6Y9x/s9Tvvv9vUa4EmXcJX4zKwU+GvgM+g8LsbngL91zvVFftY5XJw9wCPOuQ7g74Hn0XlcqP8M/Cvn3J8QDvBPoHO4WPF+j9P+u329/gdpCddFiNQeHwP+wDnXis7jYtwHfNbMngNuAD6EzuFinALWRx43E27+1XlcmFxgp5n5gVuAv0DncLHifRem/fvxulzIxcwKgReAZ4gs4boKq8B5jpn9a8JX7W9HNv0v4P9G53FRIiH+YfRZXLDIgKC/I9wsmUF4QOX30XlMmZntJfw73AC8AvwC+iwuiJk955zbZ2YNwA+Bp4HbCS8LXjd7m3MuuKzvfz0GOMRGEd4PPB9phpNF0HlcOp3D5aHzuHQ6h4sXuafHncCPoxc+8bYt63terwEuIiLiZddrH7iIiIinKcBFREQ8SAEuIiLiQQpwEQHAzH7dzPLN7AEze/+s5/7EzPaZ2Z+b2efMrMDMfhyZgiQiq0ABLiJEpsH8DuElXt8C/szMsiPP5RNeSep2oBLYSHje9bBzLmhmPjPTd4nICtMvnYgA/A3w711YJ+HVzb4ZWXO8CCgD/gDYQngN7c8CG83seeA8aVjnWUTmpwAXuc6Z2e8BvcBPotucc18ETgMvARsI17j/ivDaztWEV+76Q+C3gMecc6+vbKlFJLDaBRCR1RNpOr8XOAEcMrNyoAWI9m3/DeCAPyZ8+8m/AiYJr8h3EzABnFnhYosICnCR61pkTfsPAZjZS8AO59yfRIL9C865r5vZHcCXCd+5ajfhNdzXAf8UOcyTK15wEVETuojE/Crw48jjWsK3i8U59xLhm9g8Q6Rv3Dk3CbwJfAR4Y+WLKiKqgYsIZva7wJRz7rXIpk1EAjziK4RHqP8R0Gpm64DthJvQbwT2r2BxRQQFuMh1zcyMcFN4N+EaOGb2nwjf0OLTkZ9LCDehnyF8l6VthO9i9W+BTuAfzewTzrnTK/4PELmO6WYmItc5M8tzzg0neU3AOTcVeWyAL3prRDMzpy8SkRWnABcREfEgDWITERHxIAW4iIiIBynARUREPEgBLiIi4kH/PzOkQQcPzSTKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=girl_df[\"女800米跑分数\"]\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.hist(bins=4,x=t.values)\n",
    "# 显示横轴标签\n",
    "plt.xlabel(\"分数\")\n",
    "# plt.xticks(labels=[0,25,50,75,100])\n",
    "# 显示纵轴标签\n",
    "plt.ylabel(\"人数\")\n",
    "# 显示图标题\n",
    "plt.title(\"女子800米成绩分布直方图\",\n",
    "         fontsize = 32,\n",
    "          backgroundcolor = '#c5b782',\n",
    "          color= 'white',\n",
    "          pad=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=girls_df['女800米跑分数']\n",
    "dt1=girls_df['女跳远分数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女子跳远成绩分布直方图')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.hist(bins=4,x=dt1.values)\n",
    "# 显示横轴标签\n",
    "plt.xlabel(\"分数\")\n",
    "# plt.xticks(labels=[0,25,50,75,100])\n",
    "# 显示纵轴标签\n",
    "plt.ylabel(\"人数\")\n",
    "# 显示图标题\n",
    "plt.title(\"女子跳远成绩分布直方图\",\n",
    "         fontsize = 32,\n",
    "          backgroundcolor = '#c5b782',\n",
    "          color= 'white',\n",
    "          pad=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      25.120001\n",
       "1      17.410000\n",
       "2      16.280001\n",
       "3      23.799999\n",
       "4      18.709999\n",
       "         ...    \n",
       "471    17.959999\n",
       "472    22.440001\n",
       "473    24.260000\n",
       "474    19.840000\n",
       "475    17.480000\n",
       "Name: BMI, Length: 466, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbmi=boy_df['BMI']\n",
    "# bbmi=bbmi[bbmi['BMI']!=0]\n",
    "bbmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      19.309999\n",
       "1      25.070000\n",
       "2      24.340000\n",
       "3      19.799999\n",
       "4      22.910000\n",
       "         ...    \n",
       "588    19.629999\n",
       "589    21.490000\n",
       "590    17.850000\n",
       "591    18.379999\n",
       "592    21.070000\n",
       "Name: BMI, Length: 586, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbmi=girl_df['BMI']\n",
    "gbmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "boy_df=boy_df[boy_df['BMI']!=0]\n",
    "girl_df=girl_df[girl_df['BMI']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除bmi为0的列，因为它们没参加考试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑分数等级</th>\n",
       "      <th>男跳远分数等级</th>\n",
       "      <th>分类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>低体重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.58</td>\n",
       "      <td>50</td>\n",
       "      <td>8.76</td>\n",
       "      <td>66</td>\n",
       "      <td>200</td>\n",
       "      <td>62</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>4533</td>\n",
       "      <td>95</td>\n",
       "      <td>169</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>17.959999</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>良好(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>466 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "471  17  男     4.58         50   8.76       66  200     62    12      74    9   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI 男1000米跑分数等级     男跳远分数等级  \\\n",
       "0        0  2785      62  170  72.599998  25.120001   良好(60-80)   良好(60-80)   \n",
       "1       60  3133      68  174  52.700001  17.410000   良好(60-80)   良好(60-80)   \n",
       "2        0  3901      80  169  46.500000  16.280001   良好(60-80)   良好(60-80)   \n",
       "3        0  4946     100  183  79.699997  23.799999   良好(60-80)   良好(60-80)   \n",
       "4       68  3538      74  171  54.700001  18.709999  优秀(90-100)   良好(60-80)   \n",
       "..     ...   ...     ...  ...        ...        ...         ...         ...   \n",
       "471     68  4533      95  169  51.299999  17.959999   不及格(0-59)   良好(60-80)   \n",
       "472      0  4647     100  176  69.500000  22.440001   良好(60-80)   良好(60-80)   \n",
       "473     50  7042     100  177  76.000000  24.260000   不及格(0-59)   良好(60-80)   \n",
       "474     85  5755     100  181  65.000000  19.840000         NaN  优秀(90-100)   \n",
       "475     76  5688     100  172  51.700001  17.480000   良好(60-80)   良好(60-80)   \n",
       "\n",
       "      分类  \n",
       "0     超重  \n",
       "1     正常  \n",
       "2    低体重  \n",
       "3     超重  \n",
       "4     正常  \n",
       "..   ...  \n",
       "471   正常  \n",
       "472   正常  \n",
       "473   超重  \n",
       "474   正常  \n",
       "475   正常  \n",
       "\n",
       "[466 rows x 20 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=[0,16.45,23.25,26.35,100]\n",
    "labels=['低体重','正常','超重','肥胖']\n",
    "db=pd.cut(boy_df['BMI'],bins,include_lowest=True,\n",
    "right=False,labels=labels)\n",
    "boy_df['分类']=db\n",
    "boy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PHON\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑分数等级</th>\n",
       "      <th>男1000米跑分数等级</th>\n",
       "      <th>分类</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>良好(80-90)</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>良好(80-90)</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>良好(80-90)</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>良好(80-90)</td>\n",
       "      <td>优秀(90-100)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>及格(60-80)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>不及格(0-59)</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI 女800米跑分数等级 男1000米跑分数等级  \\\n",
       "0       85  3775     100  163.0  51.299999  19.309999        NaN         NaN   \n",
       "1       66  3683     100  163.0  66.599998  25.070000  不及格(0-59)   不及格(0-59)   \n",
       "2       76  3331     100  157.0  60.000000  24.340000  良好(80-90)  优秀(90-100)   \n",
       "3       85  3701     100  160.0  50.700001  19.799999  良好(80-90)  优秀(90-100)   \n",
       "4       70  3592     100  167.0  63.900002  22.910000  良好(80-90)  优秀(90-100)   \n",
       "..     ...   ...     ...    ...        ...        ...        ...         ...   \n",
       "588     78  2255      70  158.0  49.000000  19.629999  及格(60-80)   及格(60-80)   \n",
       "589     72  2937      85  161.0  55.700001  21.490000  及格(60-80)   及格(60-80)   \n",
       "590     72  2592      76  165.0  48.599998  17.850000  良好(80-90)  优秀(90-100)   \n",
       "591     78  1829      60  154.0  43.599998  18.379999  及格(60-80)   及格(60-80)   \n",
       "592     85  2962      85  162.0  55.299999  21.070000  不及格(0-59)   不及格(0-59)   \n",
       "\n",
       "     分类  \n",
       "0    正常  \n",
       "1    超重  \n",
       "2    超重  \n",
       "3    正常  \n",
       "4    超重  \n",
       "..   ..  \n",
       "588  正常  \n",
       "589  正常  \n",
       "590  正常  \n",
       "591  正常  \n",
       "592  正常  \n",
       "\n",
       "[586 rows x 20 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=[0,16.45,22.65,25.25,100]\n",
    "labels=['低体重','正常','超重','肥胖']\n",
    "db=pd.cut(girl_df['BMI'],bins,include_lowest=True,\n",
    "right=False,labels=labels)\n",
    "girl_df['分类']=db\n",
    "girl_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['BMI','分类']\n",
    "boys=boy_df[cols]\n",
    "gcols=['BMI','分类']\n",
    "girls=girl_df[gcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分类</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>低体重</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>正常</th>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>超重</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肥胖</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     BMI\n",
       "分类      \n",
       "低体重   13\n",
       "正常   356\n",
       "超重    59\n",
       "肥胖    38"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=boys.groupby(by='分类').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分类</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>低体重</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>正常</th>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>超重</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肥胖</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     BMI\n",
       "分类      \n",
       "低体重   10\n",
       "正常   437\n",
       "超重    91\n",
       "肥胖    48"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=girls.groupby(by='分类').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26fb1432358>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,9),dpi=100)\n",
    "p1=[23,803,150,86]\n",
    "p2=[13,10,356,437,59,91,38,48]\n",
    "labels = ['低体重','正常','超重','肥胖']\n",
    "labels2=['男','女','男','女','男','女','男','女']\n",
    "def func(pct):\n",
    "    return r'%0.1f'%(pct) + '%'\n",
    "plt.pie(p1,autopct=lambda pct: func(pct),\n",
    "radius=1, \n",
    "pctdistance=0.85, \n",
    "wedgeprops=dict(linewidth=4,width=0.4,edgecolor='w'),labels=labels)\n",
    "plt.pie(p2,autopct='%0.1f%%',radius=0.7,pctdistance=0.7,wedgeprops=dict(linewidth=4,width=0.7,edgecolor='w'),labels=labels2)\n",
    "# wedgeprops=dict(linewidth=4,width=0.4,edgecolor='w'),labels=labels)\n",
    "plt.legend(labels,loc = 'upper right',bbox_to_anchor = (0.75,0,0.4,1),title ='BMI占比')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
